Hierarchy of Evidence: A Simple System for Orthopaedic Research?
Bibliographic record
Abstract
To be able to make a sound recommendation for a treatment based on the best available evidence, it is necessary to follow specific steps in acquiring literature, appraising the study design and quality, and assessing the results. Evidence-based medicine is founded on the concepts of using best evidence, levels of evidence, and grades of recommendation, and aims to provide clinicians with standardized rules to help them appraise the validity of published research. A number of systems have been developed to categorize research studies into consistent levels of evidence. These systems are based primarily on consensus expert opinion, and have not been validated to any extent. The use of different systems does not allow for effective communication between users; there is a lack of accord even between users of the same system. The GRADE working group has devised a new rating system that attempts to address deficiencies seen within other systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.206 | 0.446 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.053 | 0.036 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.010 | 0.015 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".